Network Analysis of Sport-related Concussion Research During the Past Decade (2010–2019)
Bibliographic record
Abstract
CONTEXT: There has been substantial growth over the past decade in sport-related concussion (SRC) research, yet no research to date has synthesized developments over this critical time period. OBJECTIVE: to apply a network analysis approach to evaluate trends in the sport-related concussion (SRC) literature using a comprehensive search of original, peer-reviewed research articles involving human participants published between January 1, 2010 and December 31, 2019. DESIGN: Narrative review. MAIN OUTCOME MEASURES: Bibliometric maps were derived from a comprehensive search of all published, peer-reviewed SRC articles on the Web of Science database. A clustering algorithm was used to evaluate associations among journals, organizations/institutions, authors, and keywords. The online search yielded 6,130 articles, 528 journals, 7,598 authors, 1,966 organizations, and 3,293 keywords. RESULTS: The analysis supported five thematic clusters of journals: 1. Biomechanics/Sports medicine (n=15), 2. Pediatrics/Rehabilitation (n=15), 3. Neurotrauma/Neurology/Neurosurgery (n=11), 4. General Sports Medicine (n=11), 5. Neuropsychology (n=7). The analysis identified four organizational clusters with hub institutions: 1. University of North Carolina (n=19), 2. University of Toronto (n=19), 3. University of Michigan (n=11), 4. University of Pittsburgh (n=10). Network analysis revealed 8 clusters for SRC keywords, each with a central topic area: 1. Epidemiology (n=14), 2. Rehabilitation (n=12), 3. Biomechanics (n=11), 4. Imaging (n=10), 5. Assessment (n=9), 6. Mental health/Chronic Traumatic Encephalopathy (n=9), 7. Neurocognition (n=8), 8. Symptoms/impairments (n=5). CONCLUSIONS: The findings suggest that during the past decade SRC research has: 1) been published primarily in sports medicine, pediatric, and neuro-focused journals, 2) involved a select group of researchers from several key institutions, and 3) focused on new topic areas including treatment/rehabilitation and mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".